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analytical techniques to assess materials in both pre- and post-consumer shoes. The project aims to: Map footwear waste flows in Europe by building a database of common materials in selected footwear
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abilities, and an analytical mindset. We are seeking a candidate with the following qualifications: A strong academic record, with foundational knowledge in entrepreneurship, sustainability, innovation
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monitoring. Responsibilities Drive forward the department’s analytical and statistical expertise in remote sensing, LiDAR, AI tools, GIS, and spatial modeling. Take a leading role in developing digital tools
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monitoring. Responsibilities Drive forward the department’s analytical and statistical expertise in remote sensing, LiDAR, AI tools, GIS, and spatial modeling. Take a leading role in developing digital tools
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programming skills (R, Python, or Matlab), particularly regarding bioinformatic tools. In addition, you are expected to have insight into plant biomass composition, chemistry and analytics, some practical
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, innovative, and collaborative PhD fellow with: A recent academic education with a master’s degree in biochemistry, biomedicine, molecular biology, protein science, analytical biochemistry, or similar field
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analysis. Strong analytical skills and demonstrated ability to work effectively in a collaborative, interdisciplinary research environment. About the REMOD-HEALING Doctoral Network The Marie Sklodowska-Curie
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of prior data?) Additional research topics may include: Algorithmic Transparency and Fairness in Funding Decisions Comparative Analysis of Funding Models AI-Driven Predictive Analytics for Funding Success
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structured and analytical approach to work and problem-solving It would be an advantage to have experience with analysis equipment, e.g., HPLC, and LC-MS Good collaboration and communication skills (written
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applications Strong analytical and problem-solving skills Good communication skills and ability to collaborate across disciplines Motivation to make a real-world impact in sustainable energy and industrial